Hydroxychloroquine against COVID-19: A critical appraisal of the existing evidence
Bibliographic record
Abstract
Hydroxychloroquine (HCQ) has sparked much interest in the therapeutics of the Coronavirus Disease 2019 (COVID-19) pandemic. Its antiviral properties have been studied for years; regarding the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), it has been shown that HCQ may act at multiple levels. These extend from the initial attachment of the virus to the respiratory epithelium to the inhibition of its replication by the alkalinisation of the phagolysosome's microenvironment and the post-translational modification of certain viral proteins. Preliminary clinical evidence from China and France showed significant virological and clinical benefit in HCQ-treated patients, while other studies, mostly including critically ill patients, did not show favorable results. In this review, we critically appraise the existing evidence on HCQ against SARS-CoV-2 with particular emphasis on its protective and therapeutic role. Safety concerns that are relevant to the short-term HCQ use are also discussed. In the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".